Nursyifa Puspa Ar-rahmi Slamet
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Prediksi Jumlah Sampah pada Sektor Informal di Provinsi Jawa Barat MenggunakanAlgoritma Regresi Linear Nursyifa Puspa Ar-rahmi Slamet; Nana Suarna; Willy Prihartono
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.10294

Abstract

Waste has become one of the most pressing global problems to be solved. Rapid population growth, urbanization, and consumerism have led to a significant increase in the volume of waste worldwide. This phenomenon not only affects the environment, but also touches the economic sector, health, and social life. West Java, as one of the provinces with the highest population density in Indonesia, faces great pressure regarding waste management. The province is experiencing a significant increase in the amount of waste that occurs due to population growth and high intensity of industrial activities. The method used in this research is linear regression algorithm. The application of linear regression algorithm can help the government to plan strategic measures in waste management. By using historical data on waste production, population growth, and other factors. This algorithm can provide an overview of future trends in waste generation. The purpose of this research is to implement a linear regression algorithm to predict the amount of waste data that goes to the informal sector, especially involving collectors or stalls in West Java province. The results of this study resulted in an increase in the accuracy level of the accuracy of the volume of waste in the informal sector in West Java Province can have a significant impact and make a major contribution to the understanding of the effectiveness of the application of linear regression algorithms. This increase in accuracy is expected to deepen the understanding of how the algorithm can be optimized for more efficient prediction and management of waste volume.